Sparse-sampling with time-encoded (TICO) stimulated Raman scattering for fast image acquisition

Abstract

Modern biomedical imaging modalities aim to provide researchers a multimodal contrast for a deeper insight into a specimen under investigation. A very promising technique is stimulated Raman scattering (SRS) microscopy, which can unveil the chemical composition of a sample with a very high specificity. Although the signal intensities are enhanced manifold to achieve a faster acquisition of images if compared to standard Raman microscopy, there is a trade-off between specificity and acquisition speed. Commonly used SRS concepts either probe only very few Raman transitions as the tuning of the applied laser sources is complicated or record whole spectra with a spectrometer based setup. While the first approach is fast, it reduces the specificity and the spectrometer approach records whole spectra -with energy differences where no Raman information is present-, which limits the acquisition speed. Therefore, we present a new approach based on the TICO-Raman concept, which we call sparse-sampling. The TICO-sparse-sampling setup is fully electronically controllable and allows probing of only the characteristic peaks of a Raman spectrum instead of always acquiring a whole spectrum. By reducing the spectral points to the relevant peaks, the acquisition time can be greatly reduced compared to a uniformly, equidistantly sampled Raman spectrum while the specificity and the signal to noise ratio (SNR) are maintained. Furthermore, all laser sources are completely fiber based. The synchronized detection enables a full resolution of the Raman signal, whereas the analogue and digital balancing allows shot noise limited detection. First imaging results with polystyrene (PS) and polymethylmethacrylate (PMMA) beads confirm the advantages of TICO sparse-sampling. We achieved a pixel dwell time as low as 35 µs for an image differentiating both species. The mechanical properties of the applied voice coil stage for scanning the sample currently limits even faster acquisition.